Optimizing index file sizes based on indexed data storage conditions

ABSTRACT

Techniques and mechanisms are disclosed to optimize the size of index files to improve use of storage space available to indexers and other components of a data intake and query system. Index files of a data intake and query system may include, among other data, a keyword portion containing mappings between keywords and location references to event data containing the keywords. Optimizing an amount of storage space used by index files may include removing, modifying and/or recreating various components of index files in response to detecting one or more storage conditions related to the event data indexed by the index files. The optimization of index files generally may attempt to manage a tradeoff between an efficiency with which search requests can be processed using the index files and an amount of storage space occupied by the index files.

CROSS-REFERENCE TO RELATED APPLICATIONS

N/A.

TECHNICAL FIELD

Embodiments relate generally to managing indexed data within a computingenvironment and, more particularly, to optimizing the size of indexfiles based on storage conditions related to data indexed by the indexfiles.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

The amount of data generated by modern data centers and other computingenvironments is ever increasing. Many organizations are collectingincreasing amounts of this generated data for subsequent analysis andother purposes. However, the usefulness of such collected data is oftenlimited by users' ability to search and extract information relevant toa user's needs and as the amount of collected data grows, so too doesthe computational task of finding data relevant to a user's requestsincreases in difficulty.

One strategy for increasing the speed and accuracy with whichinformation can be retrieved is to create an index. At a high level, anindex maps a set of keys to particular values or locations within acollection of data at which the keys exist. In the context of computingenvironments, indexes may be used to increase the speed of searchrequests by mapping search keywords to locations in a set of datacontaining the keywords and/or by similarly indexing other valuescontained in the data. When a keyword-based search request is received,the index may be consulted to determine exactly where certain keywordsare located instead of searching the entire set of data for the sameinformation. However, as an amount of data stored by a computing deviceincreases and as more of the data is indexed, an amount of storage usedto store the indexes may grow to levels that significantly increase thecost of storage and operation.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates an example block diagram of a clustered data intakeand query system in accordance with the disclosed embodiments;

FIG. 19 illustrates a flow diagram of a process that may be used tooptimize the size of index files based on storage conditions related todata indexed by the index files, in accordance with the disclosedembodiments;

FIG. 20A illustrates an example diagram of an index file in accordancewith the disclosed embodiments;

FIG. 20B illustrates an example diagram of an optimized index file inaccordance with the disclosed embodiments; and

FIG. 21 illustrates a computer system upon which an embodiment may beimplemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment    -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7. Field Extraction    -   2.8. Example Search Screen    -   2.9. Data Modelling    -   2.10. Acceleration Techniques        -   2.10.1. Aggregation Technique        -   2.10.2. Keyword Index        -   2.10.3. High Performance Analytics Store        -   2.10.4. Accelerating Report Generation    -   2.11. Security Features    -   2.12. Data Center Monitoring    -   2.13. Cloud-Based System Overview    -   2.14. Searching Externally Archived Data        -   2.14.1. ERP Process Features    -   2.15. Clustered Operating Environment    -   3.0. Functional Overview    -   4.0. Example Embodiments    -   5.0. Implementation Mechanism—Hardware Overview    -   6.0. Extensions and Alternatives

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters forms a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally, or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby data intake and query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “I” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are preselected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistic may be producedby filtering the search results by the product name, finding alloccurrences of a successful purchase in a field within the events andgenerating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively, or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically, the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, SPLUNK® IT SERVICE INTELLIGENCE implementsa translation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’and ‘informational’. Thresholds associated with a particular KPIdefinition determine ranges of values for that KPI that correspond tothe various state values. In one case, KPI values 95-100 may be set tocorrespond to ‘critical’ in the state domain. KPI values from disparateKPI's can be processed uniformly once they are translated into thecommon state values using the thresholds. For example, “normal 80% ofthe time” can be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value can be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICE INTELLIGENCEcan also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE provides various visualizations built onits service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

2.15. Clustered Operating Environment

It should be appreciated that, to achieve high availability and toprovide for disaster recovery of data stored in a system, such as thedata intake and query system illustrated in FIG. 2, various componentsof the system may be configured to operate as a cluster. A clustereddata intake and query system as described herein generally may includevarious system components (e.g., forwarders, indexers, data stores,and/or search heads) configured to operate together in a coordinatedfashion. To provide for high availability and disaster recovery in aclustered system, data processed and stored by an indexer in a datastore may be replicated across one or more other indexers and datastores of the cluster according to a user configurable data replicationpolicy. In one embodiment, a specialized cluster component, referred toherein as a master node, may be configured to coordinate various aspectsof replicating data across data stores of the cluster and performingsearches against data that has been replicated in a cluster.

There are many options for how data may be replicated in a cluster and,in one embodiment, the manner in which data is replicated in aparticular cluster may be based in part on a user configurable datareplication policy. One configurable component of a data replicationpolicy may be referred to as a “replication factor.” The replicationfactor for a cluster is a value indicating a number of copies of eachgrouped subset of data, or bucket, created by an indexer that are to bestored across other indexers and in separate data stores of the cluster.For example, a cluster configured with a replication factor of two (2)indicates that for each data bucket stored by an indexer, one additionalcopy of the bucket is to be stored by a different indexer of thecluster. Similarly, a cluster configured with a replication factor offour (4) indicates that each data bucket stored by an indexer is to bereplicated at three additional indexers of the cluster. In this manner,a cluster configured with a particular replication factor generally cantolerate a concurrent failure of a number of indexers that is one lessthan the replication factor.

As indicated above, when an indexer receives data from a forwarder, theindexer may store the data as one or more grouped subsets, or buckets,each corresponding to a time range associated with the data in thebucket. Each bucket created by an indexer (or heavy forwarder) maycontain at least two types of files: event data extracted from the rawdata and, optionally, a key word index that enables searches to beperformed more efficiently on the event data. In one embodiment, eachreplicated copy of a grouped subset of data created according to a datareplication policy may either be searchable, meaning the bucket includesa copy of the key word index, or non-searchable, meaning the bucketincludes only a copy of the event data and may not be immediatelysearchable using the key word index.

To determine a number of searchable copies of each grouped subset ofdata to store in the cluster, a data replication policy configurationmay further include a “search factor.” A search factor is similar to areplication factor except that it indicates a number of searchablecopies of each bucket to store in the cluster. For example, a clustermay be configured with a search factor of one (1), indicating that onlyone of the copies of a bucket in a cluster is to include a key wordindex. However, if a search factor of greater than one is configured,some or all of the indexers storing a replicated copy of a bucket maygenerate index files for the replicated buckets, or the indexers mayreceive a copy of the index files from another indexer.

A cluster may be configured with a replication factor that is differentfrom a configured search factor. For example, a particular cluster maybe configured with a replication factor of three (3) and a search factorof two (2). According to this example data replication policy, thecluster maintains three copies of each bucket in the cluster; however,only two of the three buckets copies in the cluster include index filesand therefore are capable of responding to search requests. The indexersstoring a copy of a bucket that does not include the index files may beunable to respond to search requests for that bucket, but the bucket canbe made searchable at a later time by causing the indexer to generatethe appropriate index files or to receive the index files from anotherindexer. For example, a non-searchable copy of a bucket may be madesearchable due to one or more indexers storing a searchable copy of thebucket experiencing a failure, or based on a change to the searchfactor.

As indicated above, a cluster configured with a data replication policycauses replicated copies of each bucket created by an indexer of thecluster to be stored on other indexers of the cluster. When a searchquery is received by a search head associated with the cluster, thesearch head may distribute the search query to all of the indexers of acluster. However, if multiple indexers in the cluster store copies ofone or more buckets that contain data that partially satisfies thesearch query, duplicate search results may be returned to the searchhead. To ensure that only one indexer of a cluster returns results fromeach bucket when multiple copies of the buckets exist in the cluster,one indexer is designated as the “primary” indexer for each bucket,while other indexers storing copies of the same bucket are designated as“secondary” indexers. An indexer that is designated as the primaryindexer for a bucket has primary responsibility for returning resultsfrom that bucket that are responsive to search queries received by theprimary indexer, while secondary indexers do not respond to searchqueries with results from secondary copies of the same bucket. In otherwords, when an indexer of a cluster receives a search query from asearch head, the indexer finds events in buckets for which the indexeris the primary indexer and that satisfy the search query criteria. In analternative embodiment, the other indexers storing copies of the samebucket are simply not designated as the primary indexer for the bucket.

For each bucket that is replicated across multiple indexers of acluster, the designation of one indexer as the primary indexer and otherindexers as secondary indexers may change over time. In one embodiment,a mapping of indexers of a cluster as either a primary indexer or asecondary indexer for each bucket may be represented by the concept of a“generation.” In general, a generation represents a “snapshot” of thecluster at a particular point in time and identifies which indexers areprimary and which indexers are secondary for each bucket and replicatedcopy of a bucket stored in the cluster. A centralized “master node” ofthe cluster may be responsible for creating a generation mapping anddistributing the generation mapping to other components of the cluster.

A master node may create multiple different generations with differentmappings over time as conditions within the cluster change. Eachgeneration may be identified by a unique generation identifierrepresented, for example, by a monotonically increasing counter or otherset of unique values. For example, a first generation may be representedby a generation identifier of zero (generation 0), a second generationrepresented by a generation identifier of one (generation 1), and soforth. Thus, for a first generation 0, a particular indexer X of acluster may be designated as the primary indexer for a particular bucketZ that is replicated across a number of indexers in the cluster. At alater time, a new generation 1 may be created and a different indexer Yinstead may be designated as the primary indexer for the same bucket Z.A master node may create new generations and corresponding generationidentifiers in response to a number of different cluster eventsincluding, but limited to, any of: the master node initializing, a newindexer joining the cluster, a current indexer failing or leaving thecluster, to rebalance the buckets of a cluster, etc.

FIG. 18 shows a block diagram of an example embodiment of a clustereddata intake and query system, according to one embodiment. Similar tothe system 108 of FIG. 2, cluster 1800 includes one or more forwarders1804 that collect data from a variety of different data sources 1802 andwhich determine which indexer or indexers (e.g., one or more of indexers1806A-1806C) receive the data. An indexer 1806A-1806C receiving datafrom a forwarder 1804 may perform various operations to process, index,and store the data in a corresponding data store 1808A-1808C. The dataprocessed by an indexer 1806A-1806C may be stored in a correspondingdata store 1808A-1808C in one or more grouped subsets of data, or“buckets,” that correspond to various time ranges. For example, each ofdata stores 1808A-1808C is depicted in FIG. 18 as storing one or moreexample grouped subsets of data labeled “1A”, “1B”, “2A”, “2B”, “3A”,and “3B”. In this example, “A” and “B” versions of a grouped subset ofdata represent copies of the same data.

In cluster 1800, a search head 1810 is responsible for distributingsearch queries received from clients to indexers 1806A-1806C, and forconsolidating any search results received from the indexers. Forexample, a search head 1810 may distribute a search query to indexers1806A-1806C, where the indexers perform the actual searches against thegrouped subsets of data stored by the indexers in data stores1808A-1808C.

To perform a search against data stored by cluster 1800, in oneembodiment, a search head 1810 may first obtain information from masternode 1812, including a list of active indexers of the cluster and ageneration identifier. As indicated above, a generation identifieridentifies a particular generation mapping which indicates, for eachgrouped subset of data stored by indexers of the cluster, which of theindexers is the primary indexer and which indexers are secondaryindexers.

The search head 1810 may distribute a search query to all of the activeindexers along with the generation identifier. Each indexer receivingthe search query may use the generation identifier to identify whichgeneration mapping to consult when searching the buckets stored by theindexer. In other words, based on the generation informationcorresponding to the received generation identifier, each indexersearches for event results in grouped subsets of data for which theindexer is the primary indexer and which satisfy the search querycriteria. After processing the search query, each indexer may send aresponse to search head 1810 including either event results or anindication that the indexer has zero event results satisfying the searchcriteria. The response from each indexer may further include metadatainformation indicating an amount of time that elapsed to process thesearch and/or other diagnostic information. If a search head 1810 doesnot receive a response from one or more of the indexers to which thesearch query was distributed, the search head 1810 may generate an alertindicating that a response was not received from the indexer(s) and thatthe search results therefore may be incomplete.

Typically, a search head 1810 performs a search query with respect tothe most recent generation created by the master node. However, in somecases where one or more queries take an abnormally long time to process,it is possible that indexers of a cluster could be processing a searchquery based on a generation that is earlier than the current generation.Those same indexers could receive subsequent search queries that arebased on the current generation and therefore can concurrently processtwo separate queries based on different generations.

In one embodiment, a master node 1812 may be configured to maintain anapproximately equal number of grouped subsets of data on each indexer,and to maintain an approximately equal number of grouped subsets of datafor which each indexer has primary responsibility. Without an evendistribution of grouped subsets of data and primary indexerresponsibilities, it may be possible that individual indexers haveprimary responsibility for more grouped subsets of data than otherindexers and those indexers may become overloaded if a sufficientlylarge number of queries are submitted near in time to one another. Amaster node 1812 may periodically rebalance primary responsibilityassignments by determining how many grouped subsets of data arecurrently stored by each indexer and which indexers are designatedprimary for each grouped subset, and create a new generation where thenumber of grouped subsets of data for which each indexer has primaryresponsibility is approximately the same.

To illustrate one example of grouped subsets of data stored in anindexer cluster according to a data replication policy, each of datastores 1808A-1808C is depicted storing one or more of the bucketslabeled “1A”, “1B”, “2A”, “2B”, “3A”, and “3B”. The example cluster1800, for example, may be configured with a replication factor of two(2). In the example, an “A” version of a bucket may represent anoriginal version of the grouped subset of data, whereas a “B” versionrepresents a replicated copy of the same data. For example, indexer1806A may have received data from a forwarder 1804 which indexer 1806Aprocessed and stored in the grouped subset of data labeled 1A. Afterregistering the grouped subset of data 1A with master node 1812, andbased on data replication instructions received from the master node1812, indexer 1806A may have forwarded the data comprising groupedsubset 1A to an indexer 1806B, which then stored a copy of the data asthe grouped subset of data labeled 1B. Similarly, an indexer 1806C mayhave received data from a forwarder 1804 and stored the data in thegrouped subset of data labeled “3A”. Based on replication instructionsreceived from master node 1812, indexer 1806C forwards the datacomprising the grouped subset 3A to indexer 1806A, which then stores acopy of the data as the grouped subset of data labeled 3B.

Because the example data replication policy for cluster 1800 isconfigured with a replication factor of two (2), as illustrated in FIG.18, at least two copies of each grouped subset of data are stored ondifferent indexers within the cluster. In this manner, if any one ofindexers 1806A-1806B experiences a failure, at least one copy of eachgrouped subset of data stored by an indexer is stored by another indexerof the cluster. In response to such a failure, for example, a masternode 1812 may create a new generation that, if necessary, reorganizesthe designation of particular indexers in cluster 1808 as the primaryindexer for each bucket so that a searchable copy of each bucket isavailable without disruption. Techniques for managing data in a clusterenvironment are described in U.S. patent application Ser. No.13/648,116, filed on Oct. 9, 2012, U.S. patent application Ser. No.13/662,358, filed on Oct. 26, 2012, and U.S. Provisional PatentApplication No. 61/647,245, filed on May 15, 2012, each of which ishereby incorporated by reference in their entirety for all purposes.

3.0. Functional Overview

Approaches, techniques, and mechanisms are disclosed that enableindexers and other components of a data intake and query system tooptimize an amount of storage space occupied by index files created bythe system. As described in Section 2.5 and elsewhere herein, a dataintake and query system may include one or system components (e.g., anindexer 206) configured to ingest, index, and store data for subsequentprocessing and/or searching. The data ingested by the system mayoriginate from any number of input sources including, for example, datafiles, directories of files, data sent over a network, event logs,registries, etc. In one embodiment, a process for indexing and storingdata ingested by indexers of a data intake and query system generallymay include creating at least two types of data: “rawdata” files andindex files.

In an embodiment, “rawdata” files (or raw data) may be created fromingested data to contain event data, where events represent individualdata items derived from the ingested data and which may be associatedwith one or more timestamps. For example, if one type of data ingestedby a data intake and query system includes network traffic log files,each event contained in raw data created by an indexer may correspond toan individual network request recorded in the log files and may beassociated with one or more timestamps corresponding to when the networkrequest occurred. In one embodiment, indexers may organize the createdraw data into grouped subsets of data, or “buckets,” where each groupedsubset of data corresponds to a range of time associated with the eventscontained in the raw data.

In one embodiment, indexers of a data intake and query system mayfurther generate one or more index files used to facilitate searchingthe event data contained in the grouped subsets of data. In general,such index files may include various types of metadata that describesthe events contained within the grouped subsets of data. For example,the metadata may provide information about time ranges associated withevents stored in each grouped subset of data, information about a sourceof each event, a data format of each event, etc.

In one embodiment, index files may further include a keyword portionwhich maps keywords contained in event data to location references ofthe events in the event data that contain the keywords. In general, thekeyword portion and other metadata of the index files may be used toefficiently locate particular events contained in the raw data stored bythe indexers in response to search requests or other types of requestsfor event data. For example, an indexer may process a search request forevents within a particular time range and containing a particularkeyword by examining the metadata of one or more index files to findgrouped subsets of data within the particular time range, and by furtherlocating the keyword in a keyword portion of the one or more indexfiles. The location of events responsive to the search request may bedirectly identified based on the location references mapped to thekeyword in the index files. In many circumstances, using index files inthis way may enable the system to produce search results much fasterthan if the system searched entire grouped subsets of data for therequested keyword. However, this search efficiency generally may come atthe expense of storage space for the index files, where the morecomprehensively a set of data is indexed, the more storage space thecorresponding index files may consume. This tradeoff may be particularlyevident for keyword portions of index files, where including morekeywords and associated location reference mappings in an index file maysignificantly accelerate processing keyword-based search requests, butmay also significantly increase an overall size of the index file instorage.

In many situations, data stored and indexed by a data intake and querysystem may become less relevant to users as the data ages. For example,if one set of data ingested by a data intake and query system relates tonetwork threat information monitored by a network analyst, the analystmay frequently search the data for events that are less than a few daysold in order to monitor and respond to recent network intrusionattempts. The same analyst may search data stored by the system that ismore than a month old with much less frequency because the data is lessrelevant to the current status of the network. However, index filescreated for the months old data may continue to consume a relativelylarge amount of storage space compared to the event data itself, even ifthe index files are used infrequently to process search requests.

In one embodiment, to improve the use of storage space available toindexers and other components of a data intake and query system, thesystem may be configured to optimize an amount of storage space used byindex files. At a high level, optimizing an amount of storage space usedby index files may include removing, modifying and/or recreating variouscomponents of index files in response to detecting one or moreconditions related to the data indexed by the index files. Theoptimization of index files in this manner may attempt to manage atradeoff between an efficiency with which search requests can beprocessed using the index files and an amount of storage space occupiedby the index files to achieve that efficiency.

FIG. 19 illustrates a flow diagram of a process for optimizing the sizeof index files that index data stored by one or more indexers of a dataintake and query system. The various elements of flow 1900 may beperformed in a variety of systems, including systems such as a dataintake and query system 108 and/or a clustered data intake and querysystem 1800, described above in reference to FIG. 2 and FIG. 18. In anembodiment, each of the processes described in connection with thefunctional blocks described below may be implemented using one or morecomputer programs, other software elements, and/or digital logic in anyof a general-purpose computer or a special-purpose computer, whileperforming data retrieval, transformation, and storage operations thatinvolve interacting with and transforming the physical state of memoryof the computer.

At block 1902, an index file is generated including a keyword portion,the keyword portion associating a plurality of keywords with locationreferences to data stored in a grouped subset of data. In oneembodiment, an indexer (e.g., an indexer 206 or one of indexers1806A-1806C) may create the index file as part of a data ingestionprocess, as described above, or at any other time relative to theindexer storing raw data to which the index file relates. In anembodiment, a keyword portion of an index file associates keywordscontained in the raw data with location references to events containingthose keywords. In other examples, mappings between other types of databesides keywords may be used, and location references may refer to othertypes or ranges of data besides individual events.

At block 1904, the index file is stored in association with a groupedsubset of data. For example, in one embodiment, each grouped subset ofdata may correspond to a directory or other storage location of a filesystem accessible to the indexer, and the index file may be storedinside the directory to which the index file relates (along with theassociated raw data and possibly other metadata files). In otherexamples, the index file may be stored in a storage location that isseparate from the corresponding grouped subset of data and may include areference to the location of the corresponding grouped subset of data.In an embodiment, each grouped subset of data may be associated with oneor more separate index files, and each index file may relate to one ormore grouped subsets of data.

FIG. 20A is a diagram of an example index file 2000A that may be createdby an indexer to index raw data stored by the indexer. The particularcomponents and arrangement of index file 2000A are shown forillustrative purposes only; the processes described herein may be usedwith other types of index files and other arrangements of data withinindex files. In FIG. 20A, the index file 2000A includes a metadataportion 2002 and a keyword portion 2004A.

In one embodiment, a metadata portion 2002 of an index file may includevarious index components that describe various aspects of the indexeddata or the index file itself, including header information (e.g.,various values to align and/or reference locations of other componentswithin the index file), event host information (e.g., values indicatingnetwork hosts from which one or more events originated), event sourceinformation (e.g., values indicating data inputs from which one or moreevents originated), and event source type information (e.g., valuesindicating a format of data inputs from which events originated), amongother possible data. In an embodiment, the metadata portion 2002 mayalso include data that defines one or more ranges of time associatedwith the event data to which the index file 2000A relates, for example,based on timestamp values associated with the event data.

In an embodiment, an index file 2000A may include a keyword portion2004A, where the keyword portion includes any number of keyword tolocation reference mappings. As described above, the location referencemappings may identify particular event data, or ranges of event data,contained within associated rawdata files that include the keywords. Anindexer may initially create a keyword portion 2004A containing entriesfor all keywords present in raw data to which the index file relates, orfor some subset of the keywords contained in the raw data.

At block 1906, it is determined that one or more attributes of a groupedsubset of data stored by an indexer meet one or more index sizeoptimization conditions. As described in more detail hereinafter,attributes of a grouped subset of data that may be determined to meetone or more index size optimization conditions generally may be based onan age of the data stored in the grouped subset of data, an age of theevents represented by the grouped subset of data, a frequency with whichthe grouped subset of data is accessed, an amount of storage spaceavailable to the indexer, etc., and/or any combinations thereof. Anindexer 206 may determine that one or more grouped subsets of data meetone or more index size optimization conditions, for example, based on aperiodic monitoring of the grouped subset of data, in response to a userrequest to optimize one or more index files, in response to a specificperiod of time elapsing, or based on any other conditions. As describedin reference to block 1908 below, determining that a grouped subset ofdata meets one or more index size optimization conditions may cause anindexer to perform one or more steps to optimize related index files.

In one embodiment, one example attribute of a grouped subset of datathat may be determined to meet an index size optimization condition isan amount of time elapsed since the grouped subset of data was createdby the indexer. For example, an indexer 206 or other system componentmay record and/or monitor an amount of time that has elapsed since eachgrouped subset of data was stored by an indexer. The indexer maydetermine that when one or more particular grouped subsets of data reacha threshold age (e.g., 3 days, 2 weeks, or 6 months after creation,etc.), the data meets a condition for index size optimization. The agethreshold may be user configured, based on historical data (e.g., bymonitoring search request patterns data stored by the indexers), orbased on any other type of configuration.

In one embodiment, another example attribute of a grouped subset of datathat may be determined to meet an index size optimization condition mayinclude an amount of time elapsed since occurrence of one or more eventsrepresented by the grouped subset of data. For example, if one inputsource corresponds to a collection of network traffic data, the eventsrepresented by the data (e.g., individual network requests sent among aplurality of computing devices) may have occurred during a first timeperiod, but the data itself may not be indexed by an indexer 206 until alater time. In this example, an indexer 206 may determine that when oneor more particular events of a grouped subset of data reach a thresholdage, the data meets a condition for index size optimization.

In an embodiment, another example attribute of a grouped subset of datathat may be determined to meet an index size optimization condition mayinclude a frequency with which the grouped subset of data has beenaccessed over a period of time. For example, indexers 206 may track anumber of search or other types of requests that are received for datacontained in each grouped subset of data stored by the indexer. Based onthis data, an indexer 206 may be configured to optimize the index filesfor grouped subsets of data where fewer than a threshold number ofsearch or other requests have been received over some period of time.For example, an indexer may be configured to optimize index files forgrouped subsets of data that have received fewer than some number ofsearch requests in the past month, or based on any other frequencymeasurement.

In one embodiment, yet another example attribute of a grouped subset ofdata which may be determined to meet an index size optimizationcondition is an amount of storage space available at a storage deviceupon which the grouped subset of data is stored. For example, an indexermay monitor an amount of storage space available to the indexer as theindexer receives and processes data from one or more input sources. Ifthe indexer determines that an amount of storage space available to theindexer falls below a threshold value (e.g., a specific amount ofstorage space, or a percentage of available space), the indexer mayselect one or more index files for optimization. The index filesselected for optimization may be selected based on the size of theindividual index files (e.g., selected to optimize the largest indexfiles first), based on a relative age of the grouped subsets of data, orbased on any other criteria.

In one embodiment, yet another example attribute of a grouped subset ofdata which may be determined to meet an index size optimizationcondition is a type of storage device upon which the grouped subset ofdata is stored. For example, index files related to grouped subsets ofdata stored using particular types of storage (e.g., on a solid-statedrive) may be selected for optimization before other grouped subsets ofdata stored using other types of storage (e.g., hard disk drives).

In one embodiment, yet another example attribute of a grouped subset ofdata which may be determined to meet an index size optimizationconditions may include a particular operational state assigned to thegrouped subset of data. For example, a data intake and query system maydefine one or more operational states that are assigned to groupedsubsets of data, where the grouped subsets of data may transition fromone operational state to another as the data ages or based on otherconditions. As one particular example, a data intake and query systemmay assign an operational state labeled “hot” to grouped subsets of datathat are actively receiving new data, a “warm” operational state labelto grouped subsets of data containing recent data but that are notadding new data, and a “cold” operational state label to grouped subsetsof data that are older and may be ready for archiving. In this example,an indexer 206 may determine to optimize index files for grouped subsetsof data in response to the data reaching a “cold” or other particularoperational state.

At block 1908, in response to determining that the one or more indexsize optimization conditions are met, at least a part of the index fileis removed, modified, and/or recreated. For example, an indexer 206 mayremove, modify, and/or recreate some or all of a keyword portion 2004Aof an index file, where the keyword portion of the index file often mayrepresent the largest portion of the index file. In other examples, anindexer may remove, modify, and/or recreate one or more parts of ametadata portion 2002 or other files instead of or in addition tomodifications to keyword portion 2004A. The one or more index filesselected for optimization generally may include those that index datadetermined to meet the optimization conditions, as described inreference to block 1906.

In one embodiment, an indexer may be configured to remove the entirekeyword portion from one or more corresponding index files in responseto determining that the one or more index optimization conditions aremet. As indicated above, because the keyword portion of an index fileoften may represent the largest portion of the index file, removing theentire keyword portion may result in a significant reduction in theoverall size of the index file. The removal of the entire keywordportion may have an impact the ability of the indexer to processkeyword-based search requests. For example, as described below inreference to block 1912, an indexer that is unable to use a keywordportion of an index file to process a keyword-based search request mayinstead perform a sequential scan of the relevant raw data to findevents responsive to the search request, where a sequential scantypically may perform slower than use of a keyword index. In oneembodiment, an indexer 206 may remove a keyword portion by truncatingthe index file (e.g., truncating index file 2000A at the bottom of themetadata portion 2002), creating a new copy of the index file withoutthe keyword portion, or otherwise modifying the index file to remove thekeyword portion.

In one embodiment, rather than removing the entire keyword portion, anindexer may remove a subset of keywords from the keyword portion of theindex file in response to determining that the one or more index sizeoptimization conditions are met. For example, the subset of keywords forremoval may be selected based on a frequency of use of the subset ofkeywords in search requests or other operations. As an indexer or othersystem component receives search requests over time, for example, theindexer may record how often various keywords are included in therequests. Based on this information, an indexer may selectively removekeywords that are used more infrequently than others duringoptimization. The frequency with which keywords are used in searchrequests may be measured relative to each grouped subset data, relativeto each indexer, measured across all indexers, across different periodsof time, etc. Referring again to FIG. 20A, removing a subset of keywordsfrom the keyword portion 2004A may involve directly deleting thekeywords from the index file 2000A, creating a copy of the index file2000A with the subset of keywords omitted, or by performing any otheroperation to remove the selected keywords.

In one embodiment, a keyword portion of an index file may include afirst keyword portion and a second keyword portion, where the firstkeyword portion includes a selected set of frequently used keywords andthe second keyword portion includes other keywords. Similar to theprevious example, the set of frequently used keywords may be selectedbased on historical data tracking how many times each keyword was usedin search and/or other types of requests. For example, when an indexfile 2000A is initially created, a keyword portion 2004A initially maybe divided into a preselected first keyword portion containing keywordsknown to be frequently used, and a second keyword portion with otherless frequently used keywords. In this example, removing at least a partof the keyword portion may include removing the second keyword portionand retaining the first keyword portion. In this manner, an indexer caneasily remove the second keyword portion simply by truncating thekeyword portion at the end of the first keyword portion, rather thanremoving a subset of keywords that may be interspersed throughout thekeyword portion 2004A.

In one embodiment, in response to determining that the one or more indexsize optimization conditions are met, an indexer may modify some or allof the keyword to location reference mappings contained the keywordportion. For example, a keyword portion 2004A may initially includekeyword to location reference mappings where the location referencesrefer to individual events represented by an associated grouped subsetof data. In this way, events responsive to a keyword-based searchrequest may be returned using the direct references to the individualevents stored in associated with various keywords. However, storing aseparate location reference for each event containing each keyword inthe index may consume a significant amount of space.

In one embodiment, to reduce a number of stored location references anda corresponding size of the keyword portion 2004A, an indexer may modifythe location references to refer instead to blocks of events. Bychanging the granularity of the location references, for example, fewerlocation references may be included in the mappings for various keywordsin the index. However, because the modified index may instead returnblocks of events for requested keywords, an indexer 206 or othercomponent may additionally scan the block(s) of events returned for aparticular search request for individual events responsive to therequest, which generally may take longer than if direct event referenceswere available to the indexer. Thus, an indexer can balance a tradeoffbetween the efficiency of the search requests by modifying thegranularity with which the location references refer to the event data,where individual event references may consume more space but increasesearch processing speed, and less granular event block references mayconsume less space but decrease search processing speed.

In one embodiment, subsequent to removing or modifying at least a partof the keyword portion from the index file, as described in the examplesabove, an indexer may also recreate at least part of the keyword portionof the index in response to determining that a grouped subset of datameets one or more index size optimization conditions. For example, anindexer may determine to recreate at least a part of a previouslyoptimized index file in response to detecting that a frequency of searchrequests for the data is increasing, detecting that additional storagespace has become available, receiving input from a user requestingsearch acceleration, etc. An indexer may recreate the entire keywordportion (e.g., if the entire keyword portion was previously removed), apart of the keyword portion (e.g., if a selected set of keywordspreviously were removed), modify the indexing structure (e.g., if thekeyword to location reference mappings previously were modified), orperform any other operation to increase the size of the keyword portion.

In one embodiment, in some or all of the index file optimization processas described above, the process of optimizing an index file may involvemaintaining a complete copy of the index file until the optimizationoperation is complete. For example, in response to determining tooptimize a particular index file, an indexer may create a copy of theindex file, modify the copy of the index file, and then delete theoriginal index file after the modification is complete. In this manner,if a search request is received during the optimization process, thecomplete index can be used to process the search request in order toensure that a complete set of search results is returned. In otherexamples, a locking mechanism may be used to cause search requests towait to use a particular index file until any optimization processes arecomplete.

In an embodiment, an optimization process may include removing ormodifying one or more portions of an index file other than the keywordportion and/or one or more files other than an index file. For example,one or more parts of a metadata portion 2002 may be removed or modifiedin response to determining that one or more index size optimizationconditions are met, including metadata indicating a host, source, sourcetype, or time range information for indexed event data.

FIG. 20B is a diagram of an example index file from which at least apart of the keyword portion has been removed. For example, the indexfile 2000B may represent a modified version of the same index file asdepicted in FIG. 20A, including the same metadata portion 2002. In FIG.20B, keyword portion 2004B is depicted in dashed lines to indicate thatsome or all of the keyword portion has been removed and/or modifiedrelative to the index file 2000A. As described above, in some examplesthe entire keyword portion 2004B may be removed. In other examples, onlya portion of the keyword portion 2004B may be removed, or the structureof the mappings contained in the keyword portion 2004B may be modified.

In one embodiment, an indexer generating and storing an optimized indexfile may be part of a clustered data intake and query system (e.g., acluster 1800 as illustrated in FIG. 18). As described in Section 2.15, aclustered data intake and query system generally may be configured toreplicate grouped subsets of data across a set of peer indexers withinthe cluster according to a replication policy. In a clusteredenvironment, each indexer may operate on index files stored by theindexer in several different situations, each of which may or may not bebased on instructions received from a master node coordinating operationof the cluster. For example, each indexer of a cluster may operate on anindex file during creation of the index file, in response toinstructions from a master node to replicate an index file to one ormore peer indexers, in response to instructions to remove an index file,or in response to instructions to delete an entire grouped subset ofdata. As described in this section, optimization of an index filerepresents yet another situation in which an indexer of a cluster mayperform one or more operations relative to a stored index file.

In one embodiment, each indexer of a clustered data intake and querysystem may be configured to ensure that only one index file operation(e.g., optimization, deletion, etc.) is performed on a particular indexfile at any given time. For example, if an indexer of a clustercurrently is performing operations to optimize a particular index file,the indexer may be configured to ensure that another process does notattempt to delete or perform some other operation on the same particularindex file at the same time. In an embodiment, each indexer may preventconflicts between two or more separate index file operations by causingprocesses that operate on index files to obtain a “lock” on an indexfile and/or grouped subset of data storing the index file beforeperforming an operation, and to release the lock when the operation iscomplete. In an embodiment, if a process is unable to obtain a lock fora particular index file and/or grouped subset of data (e.g., becauseanother process is currently performing an operation on the same indexfile), the indexer may reschedule the operations for a later time.

In one embodiment, a clustered data intake and query system further maybe configured to propagate modifications made to index files (e.g.,index file optimizations, deletions, etc.) to replicated copies of thesame index file in a cluster. For example, in response to an indexer ofa cluster modifying a particular index file (e.g., creating an optimizedversion of the index file and deleting the previously existing indexfile), the indexer may send a notification of the optimization to amaster node of the cluster. In response to receiving the notification,the master node may instruct other indexers of the cluster toindependently modify copies of the same index file in the same manner(e.g., perform the same optimization process performed by the firstindexer). In other examples, an indexer generating an optimized indexfile may directly notify other indexers of the cluster of theoptimization, the indexer may directly send a copy of an optimized indexfile to other peer indexers, or any other processes may be used toreplicate optimized index files across a cluster of indexers.

Referring again to FIG. 19, at block 1910, a keyword-based searchrequest is received subsequent to removing, modifying, or recreating atleast a part of the keyword portion of the index file. For example, auser may use one or more interfaces of a data intake and query system(e.g., a search screen 600, a data visualization display, etc.) togenerate one or more search requests for data stored and indexed byindexers 206.

At block 1912, the keyword-based search request is processed using oneor more optimized index files. In one embodiment, an indexer maydetermine whether a particular search request relates to one or moreoptimized index files by examining a metadata portion of the indexfiles, where the metadata portion may contain a marker or otherindication that the index file is optimized and therefore may notinclude a complete keyword portion. In an embodiment, an indexer mayprocess a search request using an optimized index file in various waysdepending on whether the entire keyword portion was removed, only aportion of the keyword portion was removed (e.g., by retaining a set offrequently used keywords in the keyword portion of the index), orwhether the keyword portion indexing structure was modified. Forexample, if the entire keyword portion was removed, an indexer may useother metadata in the index file (e.g., time range information, sourceand source type information, etc.) to narrow down a set of event dataresponse to a search request, and may further scan the narrowed eventdata for individual events responsive to the search request. Similarly,if the indexing structure was modified such that the keyword portionrefers to blocks of event data, the indexer may search the keywordportion for responsive event data blocks and scan the blocks forindividual events responsive to the search request.

In one embodiment, in response to receiving a search request thatrelates to an optimized index file, an indexer or other component maycause display of an alert indicating to a user that the indexer isprocessing the search request using one or more optimized index files.For example, the alert may explain to a user why the user's searchrequest is not returning results as quickly as the user might expect ifthe data was fully indexed. In one embodiment, a user may be presentedwith an option to recreate the relevant index files, for example, if theuser expects to query the same data again in the future and desiresimproved search performance. In response to the user providing inputrequesting acceleration of future searches for the same data, an indexermay be configured to recreate one or more components of the indexfile(s) previously removed or modified.

5.0. Example Embodiments

Examples of some embodiments are represented, without limitation, in thefollowing clauses:

In an embodiment, a method or non-transitory computer readable mediumcomprises: storing, by an indexer, an index file in association with agrouped subset of data, the index file including a keyword portion, thekeyword portion associating a plurality of keywords with locationreferences to data stored in a grouped subset of data; determining, bythe indexer, that one or more attributes of the grouped subset of datameet one or more index size optimization conditions; in response todetermining that the one or more index size optimization conditions aremet, removing at least a part of the keyword portion from the indexfile.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the entire keyword portion is removed from the indexfile in response to determining that the one or more index sizeoptimization conditions are met.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the index file further includes a metadata portion,the metadata portion including data indicating a range of timeassociated with the grouped subset of data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the index file further includes a metadata portion,the metadata portion including data indicating, for each event of aplurality events represented by the grouped subset of data, an eventhost value indicating a network host from which the event originated, anevent source value indicating a data input from which the eventoriginated, and an event source type value indicating a format of thedata input from which the event originated.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes an amount of time elapsed since the data was indexed by theindexer.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes an amount of time elapsed since occurrence of one or moreevents represented by the grouped subset of data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes a frequency with which the grouped subset of data has beenaccessed over a period of time.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes an amount of storage space available at a storage device uponwhich the grouped subset of data is stored.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes a type of storage device upon which the grouped subset of datais stored.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more index size optimization conditionsincludes an operational state assigned to the grouped subset of data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: subsequent to removing at least a part of the keyword portionof the index file, receiving a search request including at least onekeyword; performing a sequential scan of the grouped subset of data tolocate one or more events containing the at least one keyword; returningthe one or more events as a set of search results.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein removing at least a part of the keyword portion fromthe index file includes removing a subset of the plurality of keywordsbased on a frequency of use of each keyword of the subset.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the keyword portion includes a first keyword portionand a second keyword portion, the first keyword portion including aselected set of frequently used keywords, the second keyword portionincluding other keywords; and wherein removing at least a part of thekeyword portion includes removing the second keyword portion andretaining the first keyword portion.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the location references to the data refer toindividual events represented by the grouped subset of data, and whereinremoving at least a part of the keyword portion from the index fileincludes modifying the location references to refer to blocks of events.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the location references to the data refer toindividual events represented by the grouped subset of data, and whereinremoving at least a part of the keyword portion from the index fileincludes modifying the location references to refer to blocks of events;subsequent to removing at least a part of the keyword portion from theindex file, receiving a search query including one or more keywords;searching the index file to identify one or more blocks of events thatinclude the one or more keywords; performing a sequential scan of theone or more blocks of events to identify one or more individual eventsthat contain at least one of the one or more keywords; returning the oneor more individual events as a set of search results.

In an embodiment, a method or non-transitory computer readable mediumcomprises: subsequent to removing at least a part of the keyword portionfrom the index file, receiving a search query for data stored in thegrouped subset of data; causing display of an alert indicating that thesearch query is using the modified index file.

In an embodiment, a method or non-transitory computer readable mediumcomprises: subsequent to removing at least a part of the keyword portionfrom the index file, recreating the at least part of the keyword portionof the index.

In an embodiment, a method or non-transitory computer readable mediumcomprises: subsequent to removing the at least a part of the keywordportion from the index file, determining, by the indexer, that one ormore attributes of the grouped subset of data meet one or more secondindex size optimization conditions; in response to determining that theone or more attributes of the grouped subset of data meet the one ormore second index size optimization conditions, recreating at least partof the keyword portion of the index.

In an embodiment, a method or non-transitory computer readable mediumcomprises: recreating the keyword portion of the index file in responseto receiving user input requesting to recreate the at least a part ofthe keyword portion.

Other examples of these and other embodiments are found throughout thisdisclosure.

6.0. Implementation Mechanism—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination thereof. Such special-purpose computing devices may alsocombine custom hard-wired logic, ASICs, or FPGAs with custom programmingto accomplish the techniques.

FIG. 21 is a block diagram that illustrates a computer system 2100utilized in implementing the above-described techniques, according to anembodiment. Computer system 2100 may be, for example, a desktopcomputing device, laptop computing device, tablet, smartphone, serverappliance, computing mainframe, multimedia device, handheld device,networking apparatus, or any other suitable device.

Computer system 2100 includes one or more busses 2102 or othercommunication mechanism for communicating information, and one or morehardware processors 2104 coupled with busses 2102 for processinginformation. Hardware processors 2104 may be, for example, generalpurpose microprocessors. Busses 2102 may include various internal and/orexternal components, including, without limitation, internal processoror memory busses, a Serial ATA bus, a PCI Express bus, a UniversalSerial Bus, a HyperTransport bus, an Infiniband bus, and/or any othersuitable wired or wireless communication channel.

Computer system 2100 also includes a main memory 2106, such as a randomaccess memory (RAM) or other dynamic or volatile storage device, coupledto bus 2102 for storing information and instructions to be executed byprocessor 2104. Main memory 2106 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 2104. Such instructions, whenstored in non-transitory storage media accessible to processor 2104,render computer system 2100 a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 2100 further includes one or more read only memories(ROM) 2108 or other static storage devices coupled to bus 2102 forstoring static information and instructions for processor 2104. One ormore storage devices 2110, such as a solid-state drive (SSD), magneticdisk, optical disk, or other suitable non-volatile storage device, isprovided and coupled to bus 2102 for storing information andinstructions.

Computer system 2100 may be coupled via bus 2102 to one or more displays2112 for presenting information to a computer user. For instance,computer system 2100 may be connected via an High-Definition MultimediaInterface (HDMI) cable or other suitable cabling to a Liquid CrystalDisplay (LCD) monitor, and/or via a wireless connection such aspeer-to-peer Wi-Fi Direct connection to a Light-Emitting Diode (LED)television. Other examples of suitable types of displays 2112 mayinclude, without limitation, plasma display devices, projectors, cathoderay tube (CRT) monitors, electronic paper, virtual reality headsets,braille terminal, and/or any other suitable device for outputtinginformation to a computer user. In an embodiment, any suitable type ofoutput device, such as, for instance, an audio speaker or printer, maybe utilized instead of a display 2112.

One or more input devices 2114 are coupled to bus 2102 for communicatinginformation and command selections to processor 2104. One example of aninput device 2114 is a keyboard, including alphanumeric and other keys.Another type of user input device 2114 is cursor control 2116, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 2104 and for controllingcursor movement on display 2112. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Yetother examples of suitable input devices 2114 include a touch-screenpanel affixed to a display 2112, cameras, microphones, accelerometers,motion detectors, and/or other sensors. In an embodiment, anetwork-based input device 2114 may be utilized. In such an embodiment,user input and/or other information or commands may be relayed viarouters and/or switches on a Local Area Network (LAN) or other suitableshared network, or via a peer-to-peer network, from the input device2114 to a network link 2120 on the computer system 2100.

A computer system 2100 may implement techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 2100 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 2100 in response to processor 2104 executing one or moresequences of one or more instructions contained in main memory 2106.Such instructions may be read into main memory 2106 from another storagemedium, such as storage device 2110. Execution of the sequences ofinstructions contained in main memory 2106 causes processor 2104 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2110.Volatile media includes dynamic memory, such as main memory 2106. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 2102. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2104 for execution. Forexample, the instructions may initially be carried on a magnetic disk ora solid state drive of a remote computer. The remote computer can loadthe instructions into its dynamic memory and use a modem to send theinstructions over a network, such as a cable network or cellularnetwork, as modulate signals. A modem local to computer system 2100 canreceive the data on the network and demodulate the signal to decode thetransmitted instructions. Appropriate circuitry can then place the dataon bus 2102. Bus 2102 carries the data to main memory 2106, from whichprocessor 2104 retrieves and executes the instructions. The instructionsreceived by main memory 2106 may optionally be stored on storage device2110 either before or after execution by processor 2104.

A computer system 2100 may also include, in an embodiment, one or morecommunication interfaces 2118 coupled to bus 2102. A communicationinterface 2118 provides a data communication coupling, typicallytwo-way, to a network link 2120 that is connected to a local network2122. For example, a communication interface 2118 may be an integratedservices digital network (ISDN) card, cable modem, satellite modem, or amodem to provide a data communication connection to a corresponding typeof telephone line. As another example, the one or more communicationinterfaces 2118 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. As yet anotherexample, the one or more communication interfaces 2118 may include awireless network interface controller, such as a 802.11-basedcontroller, Bluetooth controller, Long Term Evolution (LTE) modem,and/or other types of wireless interfaces. In any such implementation,communication interface 2118 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Network link 2120 typically provides data communication through one ormore networks to other data devices. For example, network link 2120 mayprovide a connection through local network 2122 to a host computer 2124or to data equipment operated by a Service Provider 2126. ServiceProvider 2126, which may for example be an Internet Service Provider(ISP), in turn provides data communication services through a wide areanetwork, such as the world wide packet data communication network nowcommonly referred to as the “Internet” 2128. Local network 2122 andInternet 2128 both use electrical, electromagnetic or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on network link 2120 and through communicationinterface 2118, which carry the digital data to and from computer system2100, are example forms of transmission media.

In an embodiment, computer system 2100 can send messages and receivedata, including program code and/or other types of instructions, throughthe network(s), network link 2120, and communication interface 2118. Inthe Internet example, a server 2130 might transmit a requested code foran application program through Internet 2128, ISP 2126, local network2122 and communication interface 2118. The received code may be executedby processor 2104 as it is received, and/or stored in storage device2110, or other non-volatile storage for later execution. As anotherexample, information received via a network link 2120 may be interpretedand/or processed by a software component of the computer system 2100,such as a web browser, application, or server, which in turn issuesinstructions based thereon to a processor 2104, possibly via anoperating system and/or other intermediate layers of softwarecomponents.

In an embodiment, some or all of the systems described herein may be orcomprise server computer systems, including one or more computer systems2100 that collectively implement various components of the system as aset of server-side processes. The server computer systems may includeweb server, application server, database server, and/or otherconventional server components that certain above-described componentsutilize to provide the described functionality. The server computersystems may receive network-based communications comprising input datafrom any of a variety of sources, including without limitationuser-operated client computing devices such as desktop computers,tablets, or smartphones, remote sensing devices, and/or other servercomputer systems.

In an embodiment, certain server components may be implemented in fullor in part using “cloud”-based components that are coupled to thesystems by one or more networks, such as the Internet. The cloud-basedcomponents may expose interfaces by which they provide processing,storage, software, and/or other resources to other components of thesystems. In an embodiment, the cloud-based components may be implementedby third-party entities, on behalf of another entity for whom thecomponents are deployed. In other embodiments, however, the describedsystems may be implemented entirely by computer systems owned andoperated by a single entity.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods. In an embodiment, anon-transitory computer readable storage medium, storing softwareinstructions, which when executed by one or more processors causeperformance of any of the foregoing methods.

7.0. Extensions and Alternatives

As used herein, the terms “first,” “second,” “certain,” and “particular”are used as naming conventions to distinguish queries, plans,representations, steps, objects, devices, or other items from eachother, so that these items may be referenced after they have beenintroduced. Unless otherwise specified herein, the use of these termsdoes not imply an ordering, timing, or any other characteristic of thereferenced items.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. In this regard, although specific claim dependencies are setout in the claims of this application, it is to be noted that thefeatures of the dependent claims of this application may be combined asappropriate with the features of other dependent claims and with thefeatures of the independent claims of this application, and not merelyaccording to the specific dependencies recited in the set of claims.Moreover, although separate embodiments are discussed herein, anycombination of embodiments and/or partial embodiments discussed hereinmay be combined to form further embodiments.

Any definitions expressly set forth herein for terms contained in suchclaims shall govern the meaning of such terms as used in the claims.Hence, no limitation, element, property, feature, advantage or attributethat is not expressly recited in a claim should limit the scope of suchclaim in any way. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method, comprising: storing, by an indexer, anindex file in association with a grouped subset of data, the index fileincluding a keyword portion, the keyword portion associating a pluralityof keywords with location references to data stored in the groupedsubset of data; determining, by the indexer, that one or more attributesof the grouped subset of data meet one or more index size optimizationconditions; in response to determining that the one or more index sizeoptimization conditions are met, removing at least a part of the keywordportion from the index file.
 2. The method of claim 1, wherein theentire keyword portion is removed from the index file in response todetermining that the one or more index size optimization conditions aremet.
 3. The method of claim 1, wherein the index file further includes ametadata portion, the metadata portion including data indicating a rangeof time associated with the grouped subset of data.
 4. The method ofclaim 1, wherein the index file further includes a metadata portion, themetadata portion including data indicating, for each event of aplurality events represented by the grouped subset of data, an eventhost value indicating a network host from which the event originated, anevent source value indicating a data input from which the eventoriginated, and an event source type value indicating a format of thedata input from which the event originated.
 5. The method of claim 1,wherein the one or more index size optimization conditions includes anamount of time elapsed since the data was indexed by the indexer.
 6. Themethod of claim 1, wherein the one or more index size optimizationconditions includes an amount of time elapsed since occurrence of one ormore events represented by the grouped subset of data.
 7. The method ofclaim 1, wherein the one or more index size optimization conditionsincludes a frequency with which the grouped subset of data has beenaccessed over a period of time.
 8. The method of claim 1, wherein theone or more index size optimization conditions includes an amount ofstorage space available at a storage device upon which the groupedsubset of data is stored.
 9. The method of claim 1, wherein the one ormore index size optimization conditions includes a type of storagedevice upon which the grouped subset of data is stored.
 10. The methodof claim 1, wherein the one or more index size optimization conditionsincludes an operational state assigned to the grouped subset of data.11. The method of claim 1, further comprising: subsequent to removing atleast a part of the keyword portion of the index file, receiving asearch request including at least one keyword; performing a sequentialscan of the grouped subset of data to locate one or more eventscontaining the at least one keyword; returning the one or more events asa set of search results.
 12. The method of claim 1, wherein removing atleast a part of the keyword portion from the index file includesremoving a subset of the plurality of keywords based on a frequency ofuse of each keyword of the subset.
 13. The method of claim 1, whereinthe keyword portion includes a first keyword portion and a secondkeyword portion, the first keyword portion including a selected set offrequently used keywords, the second keyword portion including otherkeywords; and wherein removing at least a part of the keyword portionincludes removing the second keyword portion and retaining the firstkeyword portion.
 14. The method of claim 1, wherein the locationreferences to the data refer to individual events represented by thegrouped subset of data, and wherein removing at least a part of thekeyword portion from the index file includes modifying the locationreferences to refer to blocks of events.
 15. The method of claim 1,further comprising: wherein the location references to the data refer toindividual events represented by the grouped subset of data, and whereinremoving at least a part of the keyword portion from the index fileincludes modifying the location references to refer to blocks of events;subsequent to removing at least a part of the keyword portion from theindex file, receiving a search query including one or more keywords;searching the index file to identify one or more blocks of events thatinclude the one or more keywords; performing a sequential scan of theone or more blocks of events to identify one or more individual eventsthat contain at least one of the one or more keywords; returning the oneor more individual events as a set of search results.
 16. The method ofclaim 1, further comprising: subsequent to removing at least a part ofthe keyword portion from the index file, receiving a search query fordata stored in the grouped subset of data; causing display of an alertindicating that the search query is using the modified index file. 17.The method of claim 1, further comprising, subsequent to removing atleast a part of the keyword portion from the index file, recreating theat least part of the keyword portion of the index.
 18. The method ofclaim 1, further comprising: subsequent to removing the at least a partof the keyword portion from the index file, determining, by the indexer,that one or more attributes of the grouped subset of data meet one ormore second index size optimization conditions; in response todetermining that the one or more attributes of the grouped subset ofdata meet the one or more second index size optimization conditions,recreating at least part of the keyword portion of the index.
 19. Themethod of claim 1, further comprising recreating the keyword portion ofthe index file in response to receiving user input requesting torecreate the at least a part of the keyword portion.
 20. The method ofclaim 1, further comprising: wherein the indexer is associated with anindexer cluster, the indexer cluster comprising a plurality of peerindexers and a master node; in response to determining that the one ormore index size optimization conditions are met, sending, to the masternode, a request to obtain a lock on the grouped subset of data; inresponse to receiving a lock on the grouped subset of data, removing atleast a part of the keyword portion from the index file; sending, to themaster node, a request to release the lock on the grouped subset ofdata.
 21. The method of claim 1, further comprising: wherein the indexeris associated with an indexer cluster, the indexer cluster comprising aplurality of peer indexers and a master node; in response to removing atleast a part of the keyword portion from the index file, sending anotification to the master node indicating that the indexer removed atleast a part of the keyword portion from the index file.
 22. Anon-transitory computer readable storage medium, storing instructions,which when executed by one or more processors causes: storing, by anindexer, an index file in association with a grouped subset of data, theindex file including a keyword portion, the keyword portion associatinga plurality of keywords with location references to data stored in thegrouped subset of data; determining, by the indexer, that one or moreattributes of the grouped subset of data meet one or more index sizeoptimization conditions; in response to determining that the one or moreindex size optimization conditions are met, removing at least a part ofthe keyword portion from the index file.
 23. An apparatus, comprising: asubsystem, implemented at least partially in hardware, that stores anindex file in association with a grouped subset of data, the index fileincluding a keyword portion, the keyword portion associating a pluralityof keywords with location references to data stored in the groupedsubset of data; a subsystem, implemented at least partially in hardware,that determines, by the indexer, that one or more attributes of thegrouped subset of data meet one or more index size optimizationconditions; a subsystem, implemented at least partially in hardware,that in response to determining that the one or more index sizeoptimization conditions are met, removes at least a part of the keywordportion from the index file.